A method and system for predicting soil and/or plant condition in precision agriculture with a classification of measurement data for providing an assignment of a measurement parcel to classes of interest. The assignment is used for providing action recommendations, particularly in real time or close to real time, to a farmer and/or to an agricultural device based on acquired measurement data, particularly remote sensing data, and wherein a classification model is trained by a machine learning algorithm, e.g. relying on deep learning for supervised and/or unsupervised learning, and is potentially continuously refined and adapted thanks to a feedback procedure.

A method and system for predicting soil and/or plant condition in precision agriculture with a classification of measurement data for providing an assignment of a measurement parcel to classes of interest. The assignment is used for providing action recommendations, particularly in real time or close to real time, to a farmer and/or to an agricultural device based on acquired measurement data, particularly remote sensing data, and wherein a classification model is trained by a machine learning algorithm, e.g. relying on deep learning for supervised and/or unsupervised learning, and is potentially continuously refined and adapted thanks to a feedback procedure.

The present disclosure relates to an inspection apparatus, a sensing apparatus, a sensitivity control apparatus, an inspection method, and a program that perform inspection with improved accuracy. The inspection apparatus includes a detection section for detecting a plurality of different wavelength region components of ambient light reflected from an inspection target to be inspected, and a control section for controlling the sensitivity of each of the different wavelength region components. The control section controls the sensitivity by calculating a histogram indicating the detection level in every wavelength region of light reflected from the inspection target that is detected by the detection section, and determining, based on histograms of particular spectroscopic components, whether or not the sensitivity is properly set for the detection section. The present technology is applicable, for example, to an inspection apparatus that inspects vegetation.

The present disclosure relates to an inspection apparatus, a sensing apparatus, a sensitivity control apparatus, an inspection method, and a program that perform inspection with improved accuracy. The inspection apparatus includes a detection section for detecting a plurality of different wavelength region components of ambient light reflected from an inspection target to be inspected, and a control section for controlling the sensitivity of each of the different wavelength region components. The control section controls the sensitivity by calculating a histogram indicating the detection level in every wavelength region of light reflected from the inspection target that is detected by the detection section, and determining, based on histograms of particular spectroscopic components, whether or not the sensitivity is properly set for the detection section. The present technology is applicable, for example, to an inspection apparatus that inspects vegetation.

The present technique relates to an image processing apparatus, an image processing method, and a program that can easily execute a stitching process.

Provided are: an image generation unit that generates a first reference image regarding a first imaging region on the basis of a plurality of first images regarding the first imaging region and that generates a second reference image regarding a second imaging region at least partially overlapping with the first imaging region on the basis of a plurality of second images regarding the second imaging region; and a processing unit that generates positioning information indicating a correspondence between the first imaging region and the second imaging region on the basis of the first reference image and the second reference image. The present technique can be applied to, for example, an image processing apparatus that executes a stitching process of a plurality of images.

A formulation and a composition that comprises it for promoting the pollination of almond tree crops (Prunus dulcis) by biasing the foraging preferences of the honey bee (Apis mellifera). The formulation comprises the compounds limonene, linalool and benzaldehyde. Additionally, a method for targeting the bees' pollinizing activity towards the almond tree crops by using the formulation comprising the compounds limonene, linalool and benzaldehyde.

A formulation and a composition that comprises it for promoting the pollination of almond tree crops (Prunus dulcis) by biasing the foraging preferences of the honey bee (Apis mellifera). The formulation comprises the compounds limonene, linalool and benzaldehyde. Additionally, a method for targeting the bees' pollinizing activity towards the almond tree crops by using the formulation comprising the compounds limonene, linalool and benzaldehyde.

One variation of a method for identifying stressors in crops based on fluorescence of sensor plants includes: accessing a set of spectral images of a sensor plant sown in a crop, the sensor plant of a sensor plant type including a set of promoters and a set of reporters configured to signal a set of stressors present at the sensor plant, the set of promoters and set of reporters forming a set of promoter-reporter pairs; accessing a reporter model linking characteristics extracted from the set of spectral images of the sensor plant to the set of stressors based on signals generated by the set of promoter-reporter pairs in the sensor plant type; and identifying a first stressor, in the set of stressors, present at the sensor plant based on the reporter model and characteristics extracted from the set of spectral images.

An integrated system for detecting a red palm weevil (RPW), farm fire, and soil moisture includes an optical fiber configured to be extending to a tree, and a distributed acoustic sensor (DAS) box connected to the optical fiber. The DAS box is configured to process first to third different optical signals reflected from the optical fiber, to determine a presence of the RPW from the first optical signal, a temperature at a location along the optical fiber from the second optical signals, and a moisture at a location around the tree from the third optical signal.

An integrated system for detecting a red palm weevil (RPW), farm fire, and soil moisture includes an optical fiber configured to be extending to a tree, and a distributed acoustic sensor (DAS) box connected to the optical fiber. The DAS box is configured to process first to third different optical signals reflected from the optical fiber, to determine a presence of the RPW from the first optical signal, a temperature at a location along the optical fiber from the second optical signals, and a moisture at a location around the tree from the third optical signal.